Persons with stroke typically experience progressive loss of aerobic capacity that can further limit their ability to perforn activities of daily living (ADL) and instrumental activities of daily living (IADL). Previous studies suggest that aerobic activity may help improve functional aerobic capacity, which in turn can lead to gains in functional independence, quality of life and cognitive functioning. The present study aims to assess the effects of aerobic exercise as a rehabilitation strategy for stroke survivors, focusing on fitness, safety, functional, cognitive and quality of life outcomes. The PIs hypothesize a dose-response relationship between aerobic exercise and functional outcomes. They further hypothesize that exercise intensity will have a greater impact on these outcomes than exercise duration. To test these hypotheses, they will collect data from a sample of 90 individuals with unilateral stroke with residual weakness and/or spasticity. Subjects will be randomly assigned to one of three conditions: (a) a 14-week 3x/week moderate intensity (50-70% VO2 reserve) exercise program, (b) a 14-week 3x/week duration-oriented exercise program at a low intensity (40% VO2 reserve), and (c) a 14-week standard-of-care condition consisting of activities aimed at improving gait, balance and range of motion. To be eligible to participate in the study, subjects will be post-stroke at least six months, 30 to 70 years of age, independently ambulatory (with or without an assistive aid), cognitively oriented and have permission from their physician to participate in the study. Prospective subjects will undergo a graded exercise test and blood test to screen out individuals with significant cardiac symptoms that would preclude exercise. Data will be analyzed using a generalized estimating equation (GEE) approach. The GEE approach was chosen for its ability to handle missing data, time-varying explanatory variables and correlations among measurements made over multiple time points that would ordinarily lead to bias in error variance estimates. Unlike traditional analytic methods, the GEE approach can be used with data characterized by almost any type of statistical distribution (e.g., normal/Gaussian, binomial, Poison).